Is a cloud environment really cheaper?
Conversations with Experts
Traffic and Chaos Engineering Manager, Netflix
Casey Rosenthal is currently the Traffic and Chaos Engineering Manager at Netflix. His teams fortify availability in anticipation of failures on one of the largest distributed systems in production. In his past as an Executive Manager, Senior Architect, and Software Engineer, Casey has managed teams to tackle Big Data, architect solutions to challenging distributed systems problems, and train others to do the same. He leverages experience with distributed systems, artificial intelligence, translating novel algorithms and academia into working models, and selling a ‘vision of the possible’ to clients and colleagues alike. For fun, he models human behavior using personality profiles in languages like Elixir, Ruby, Erlang, Prolog, and Scala.
Casey has worked on large sets of unstructured data for many years, from managing NASA’s metadata on images from heritage telescopes to more recently being the General Manager for Professional Services at Basho, whose unstructured, NoSQL database Riak is used by over a third of the Fortune 50.
Chief Architect, Cedexis
Josh Gray has served as Chief Technology Officer, Chief Architect, Head of Product and advised/consulted on various data science and analytics system projects. His experience includes analytics systems for energy, quantitative marketing, consumer preferences & behavior, retail, payments, distributed system monitoring, networking and Internet performance spaces. His projects have involved successfully delivering actionable information from petabytes of streaming data with sub-minute global latencies. He is an active follower of and contributor to big data analytics tools and practices, applying many in real data systems.
Josh’s professional passions also include development of the teams and people that execute effectively on data analytics projects and systems. He actively speaks, writes and mentors on topics of leadership, team and individual development. He is also an avid practitioner of agile project management and finds the principles are often ideally suited for maximum project velocity and agility while maximizing team member satisfaction.
As companies consider incorporating enterprise-level cloud services offered by Amazon, Microsoft, Teradata, and others, many challenges arise related to data security, integration into existing data architectures, and analytics activities themselves. Marc Clark, Teradata’s Director of Cloud Strategy and Deployment, and Dan Magestro, (now former) IIA Research Director, discuss these and other considerations for evaluating and adopting a cloud strategy for your enterprise, and what it means for analytics.
Inquiry: Yes, moving data to the cloud is cheaper than housing it internally, but how should companies consider the risks like data breaches? Are cloud environments mature enough? The reward of cheaper storage, at this point in time, does not seem to out-weigh the risk of leaking data. What are other retail organizations who have an omni-channel strategy implementing in the cloud environment for its data management, analytics, and marketing execution? How are they approaching the management of sensitive consumer information that is PII? What other pros and cons exist where saas, paas, and iaas exist? How do companies decide on length of consumer data retention? What’s best practice?
Inquiry: We are inquiring about performing high volume computational analytics in the cloud with full PHI / PII data. Many healthcare organizations are storing PHI and PII data in on premise clouds (really not much different than servers in a data center). Fewer, but a significant number of providers, are using Vendor private clouds, similar to the first, but at the vendor’s facility rather than the healthcare provider’s facility. We are doing this with our new Cerner EMR.
We currently are exploring the option of storing, moving and analyzing PHI and PII data stored and processed in a “private cloud” running on Amazon or Google.
Can you find examples of this third case of a private cloud on Amazon or Google running full PHI and PII data? The reason this becomes important (beyond cost and speed) is that it provides the ability to scale on demand for large computational tasks. We are hesitant to move in this direction without proven examples of where this has been done to build our case, especially in light of high profile data breaches.
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